Modeling temporal information encoding by the population of fibers in the healthy and synaptopathic auditory nerve.

We report a theoretical study aimed at investigating the impact of cochlear synapse loss (synaptopathy) on the encoding of the envelope (ENV) and temporal fine structure (TFS) of sounds by the population of auditory nerve fibers. A computational model was used to simulate auditory-nerve spike trains evoked by sinusoidally amplitude-modulated (AM) tones at 10 Hz with various carrier frequencies and levels. The model included 16 cochlear channels with characteristic frequencies (CFs) from 250 Hz to 8 kHz. Each channel was innervated by 3, 4 and 10 fibers with low (LSR), medium (MSR), and high spontaneous rates (HSR), respectively. For each channel, spike trains were collapsed into three separate 'population' post-stimulus time histograms (PSTHs), one per fiber type. Information theory was applied to reconstruct the stimulus waveform, ENV, and TFS from one or more PSTHs in a mathematically optimal way. The quality of the reconstruction was regarded as an estimate of the information present in the used PSTHs. Various synaptopathy scenarios were simulated by removing fibers of specific types and/or cochlear regions before stimulus reconstruction. We found that the TFS was predominantly encoded by HSR fibers at all stimulus carrier frequencies and levels. The encoding of the ENV was more complex. At lower levels, the ENV was predominantly encoded by HSR fibers with CFs near the stimulus carrier frequency. At higher levels, the ENV was equally well or better encoded by HSR fibers with CFs different from the AM carrier frequency as by LSR fibers with CFs at the carrier frequency. Altogether, findings suggest that a healthy population of HSR fibers (i.e., including fibers with CFs around and remote from the AM carrier frequency) might be sufficient to encode the ENV and TFS over a wide range of stimulus levels. Findings are discussed regarding their relevance for diagnosing synaptopathy using non-invasive ENV- and TFS-based measures.